In Space Situational Awareness (SSA), distant and dim unresolved objects are evaluated to assess configuration, activity and level of threat. Observed signatures depend on the specific illumination and viewing conditions, the materials of the space-borne object and the object orientation. Since the object is unresolved, characterization is often limited to analysis of the resulting (or, a resultant) light curve, which represents the temporal signature variations. The observed signature, or light curve, associated with an object changes as that object spins or otherwise changes in time. This temporal evolution of the light curve provides a fingerprint which can be used to assess the state of the satellite (object). While increasing the integration time may be used to increase the signal of dim objects, the integration time is limited by the temporal resolution required to resolve salient features of the light curve, thus placing a premium on sensor efficiency.
Although light curves are established as a powerful tool in SSA, other aspects of optical wavefronts, beyond the reach of traditional SSA, remain unexploited.
Accordingly, a need exists for a high-efficiency approach to spectral imaging that augments light curve analysis with spectral information, an approach conceived specifically with the efficiency and temporal sampling requirements of SSA in mind, with the goal of extracting as much information as possible from the optical wavefronts presented by distant, dim unresolved objects.